Myocardial hypoxia-inducible HIF-1α, VEGF, and GLUT1 gene expression is associated with microvascular and ICAM-1 heterogeneity during endotoxemia
Bibliographic record
Abstract
The systemic inflammatory response to infection is the leading cause of mortality in North American intensive-care units. Although much is known about inflammatory mediators, the relationships between microregional inflammation, microvascular heterogeneity, hypoxia, hypoxia-inducible gene expression, and myocardial dysfunction are unknown. Male Sprague-Dawley rats were injected intraperitoneally with LPS to test the hypothesis that sepsis-induced local inflammation and increased microvascular heterogeneity are spatially and temporally associated with hypoxia, hypoxia-inducible gene expression, and decreased left-ventricular contractility. Using a combination of three-dimensional microvascular imaging, tissue Po(2), and pressure-volume conductance measurements, we found that 5 h after LPS, minimum oxygen-diffusion distances increased (P < 0.05), whereas tissue oxygenation and contractility both decreased (P < 0.05) in the left ventricle. Real-time RT-PCR analysis revealed that the hypoxia-inducible genes hypoxia-inducible factor (HIF)-1alpha, VEGF, and glucose transporter (GLUT) 1 were all upregulated (P < 0.05) in the left ventricle. Tissue regions expressing ICAM-1, obtained by using laser-capture microdissection, had increased HIF-1alpha and GLUT1 (P < 0.05) gene expression. VEGF gene expression was more diffuse. In LPS rats, GLUT1 gene expression correlated (P < 0.05) with left-ventricular contractility. In 5-h hypoxic cardiomyocytes, we found strong transient HIF-1alpha, weak VEGF, and greater prolonged GLUT1 gene expression. By comparison, the HIF-1alpha-GLUT1 gene-induction pattern was reversed in the left ventricle of LPS rats. Together, these results show that LPS induces hypoxia in the left ventricle associated with increased microvascular heterogeneity and decreased contractility. HIF-1alpha and GLUT1 gene induction are related to a heterogeneous ICAM-1 expression and may be cardioprotective during the onset of septic injury.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".